Jamming and shutdown of tunnel boring machines(TBMs)
and rock collapse often occur during tunneling due to unknown geological
conditions. Therefore, the main mechanical parameters, such as rock mass
integrity index, uniaxial compressive strength, internal friction angle,
cohesion, deformation modulus, Poisson′s ratio, robustness
coefficient, and elastic resistance coefficient, are considered as references.
The fully adaptive noise ensemble empirical mode decomposition (CEEMDAN) and
time convolutional neural network (TCN)are used to establish a method for predicting
the grade of the surrounding rock according to the principle of
"decomposition first and then reconstruction". The results reveal the
following: (1) The mean square error between the predicted and actual values of
the surrounding rock grade based on the CEEMDANTCN
combined model is <0.07, the root mean square error is <1.67, the average
absolute percentage error is <0.45, and the average absolute error is
<0.14, with a fitting coefficient of 95.2%. (2) The CEEMDANTCN combination model has the advantages of low error, good fitting
effect, and high practicality, accurately predicting the grade of surrounding
rock at the tunnel face and achieving intelligent classification of surrounding
rock types, which is crucial for efficient TBM tunneling and early risk
warning.
QIAO Jinli
,
CHEN Shuai
,
CHEN Xiaoqiang
,
HAO Gangli
,
HU Jianbang
,
SUN Yongtao
. Prediction and Evaluation of Safety Grade of
Surrounding Rock Using CEEMDANTCN
Combination Model[J]. Tunnel Construction, 2023
, 43(9)
: 1485
-1491
.
DOI: 10.3973/j.issn.2096-4498.2023.09.005
